• Journal of Species Research
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• v.11 no.1
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• pp.10-21
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• 2022

Two strains of cyanobacteria were isolated from the soil of Seodaemun-gu, Seoul and from the gravel of the Ansung Stream, Gyeonggi Province, Korea, respectively. They were identified as Pantanalinema rosaneae and Alkalinema pantanalense under the Leptolyngbyaceae through the morphological, ecological, and molecular analyses and first reported in Korea. Belonging to the Leptolyngbya morphotypes, they are thin filamentous cyanobacteria and morphologically indistinguishable cryptic species. The strains of P. rosaneae and A. pantanalense isolated in Korea revealed the same cluster as their type species in the phylogenetic analysis using the 16S rRNA gene sequences, and similarities in the secondary structures of 16S-23S ITS sequences. Although both P. rosaneae and A. pantanalense were collected from water samples in the Pantanal wetland of Brazil, the P. rosaneae obtained in Korea, was soil-dwelling subaerophytic species whereas A. pantanalense was epilithic species living on gravel in the freshwater. Therefore, they are considered to have an extensive habitat.

• ETRI Journal
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• v.44 no.2
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• pp.232-243
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• 2022

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.10-18
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• 2022

Since High Sierra, APFS has been used as the main file system. It is a well-established file system that has been used stably thus far. From the perspective of digital forensics, there are still many areas to be investigated. Apple File System Reference is provided to the apple developer site, but it is not satisfactory to fully analyze APFS. Researchers know more about the structure of APFS than before, but they have not yet fully analyzed its structure to a perfect level about it. In this paper, we develop APFS object identification tool for digital forensics. The most basic and essential object identification and analysis of the APFS filesystem will be conducted with the tool. The analysis in this study serves as the background for an analysis of the checkpoint operation principle and structure, including the more complex B-tree structure of APFS. There are several options for the developed tool, but the results of two use cases will be shown here. Based on the implemented tool, it is hoped that more functions will be added to make APFS a useful tool for faster and more accurate analyses.

• Phonetics and Speech Sciences
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• v.14 no.1
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• pp.29-36
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• 2022

The English word-final /rl/ cluster poses a particular problem for Korean learners of English because it is the sequence of two sounds, /r/ and /l/, which are not contrastive in Korean. This study compared the similarity distances between English and Korean speakers' /rl/ productions using the dynamic time warping (DTW) algorithm. The words with /rl/ (pearl, world) and without /rl/ (bird, word) were recorded by four English speakers and four Korean speakers, and compared pairwise. The F2-F1 trajectories, the acoustic correlate of velarized /l/, and F3 trajectories, the acoustic correlate of /r/, were examined. Formant analysis showed that English speakers lowered F2-F1 values toward the end of a word, unlike Korean speakers, suggesting the absence of /l/ in Korean speakers. In contrast, there was no significant difference in F3 values. Mixed-effects regression analyses of the DTW distances revealed that Korean speakers produced /r/ similarly to English speakers but failed to produce the velarized /l/ in /rl/ clusters.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.150-153
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• 2015

The construction industry is an integral part of any nation's economy, whether measured by dollar volume or workforce size. In spite of its strong influence, there has been very little specifically aimed at evaluating the current industry performance. This research investigates the macroeconomic performance of the construction industry by accounting for crucial performance affecting factors such as labor productivity and gross margin. A clustering analysis, followed by a series of statistical analyses, yielded a notable finding that labor productivity is the most important factor that affects industry's profitability. The results of the analysis also revealed that the states with the strongest labor productivity show the highest level of profitability in terms of gross margin. This study should be of value to decision-makers when plotting a roadmap for future growth and rendering a strategic business decision.

• Mycobiology
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• v.51 no.5
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• pp.313-319
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• 2023

During disease surveys of Angelica acutiloba plants in Korea, leaf spot symptoms were observed in a field in Andong in July 2019, and stem rot symptoms in vinyl greenhouses in Yangpyeong in April 2020. Incidence of leaf spot and stem rot of the plants ranged from 10 to 20% and 5 to 30%, respectively. Morphological and cultural characteristics of fungal isolates from the leaf spot and stem rot symptoms fitted into those of the genus Phoma. Molecular phylogenetic analyses of two single-spore isolates from the symptoms using concatenated sequences of LSU, ITS, TUB2, and RPB2 genes authenticated an independent cluster from other Didymella (anamorph: Phoma) species. Moreover, the isolates showed different morphological and cultural characteristics in comparison to closely related Didymella species. These discoveries confirmed the novelty of the isolates. Pathogenicity of the novel Didymella species isolates was substantiated on leaves and stems of A. acutiloba through artificial inoculation. Thus, this study reveals that Didymella acutilobae sp. nov. causes leaf spot and stem rot in Angelica acutiloba.

• Journal of the Korea Fashion and Costume Design Association
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• v.20 no.1
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• pp.183-197
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• 2018

This study examined outdoor sports wear purchase behaviors among middle-aged male consumers based on outdoor sports wear shopping orientation. Data research was conducted on 300 internet users in their 40s and 50s located all parts of the country. The SPSS 24.0 software program was used to conduct data analyses such as descriptive statistics, frequency analysis, factor analysis, cluster analysis, $x^2-test$, t-test, ANOVA, and Duncan test as a post-hoc analysis. The results of this study were as follows: Firstly, outdoor sports wear shopping orientation was identified with fivefactors : the tendencies of wanting to show off a brand name, conservative purchasing, economical purchasing setting a high value on a salesperson, and impulse purchasing. Secondly, the middle-aged male consumers were classified in to three groups by the cluster analysis: a rational group, an indifferent shopping group, and pursuit brand shopping group. Thirdly, the evaluation criteria of products were significantly different depending on outdoor sports wear shopping orientation subdivision in all factors. Fourthly, in the case of fashion information sources regarding outdoor sportswear, significant differences were found according to shopping orientation subdivision in mass media/store source, personal source/ prior shopping experience. Fifthly, all types of stores were significantly different depending on shopping orientation subdivision except for large discount stores.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.113-124
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• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.39-52
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• 2006

This study divided the large-scale traffic accident locations into its own characteristics by using Cluster Analysis. Also, Quantification II and Classification and Regression Tree methods were used enabling evaluation for the amount of affecting influence by the crash type. After these analyses, we tested the fitness of the results and suggested the simplification of the quantification index. With the results from the discussed procedure, obvious differences were observed by groups according to the characteristics of crash type from the Discrimination and Classification analysis of divided four groups. Thus, measures and supplementary measures for the traffic accidents could be suggested in groups systematically. However, a lot of missing values in variables caused a huge loss of data and made this study difficult for more detailed analysis, With this difficulty. recording mandatory log files with a standardized format is also recommended to Prevent this Problem in advance.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.84-93
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• 2012

The study was conducted to analyze the spatio-temporal changes in water quality of the major 36 sampling stations of Nakdong River, depending on each station, season using the 17 water quality variables from 2000 to 2010. The result was verified to interpret the characteristics of water quality variables in a more accurate manners. According to the Principal component analysis (PCA) and Exploratory factor analysis (EFA) results; the results of these analyses were identified 4 factors, Factor 1 (nutrients) included the concentrations of T-N, T-P, $NO_{3}-N$, $PO_{4}-P$, DTN, DTP for sampling station and season, Factor 2 (organic pollutants) included the concentrations of BOD, COD, Chl-a, Factor 3 (microbes) included the concentrations of F.Coli, T.Coli, and Factor 4 (others) included the concentrations of pH, DO. The results of a Cluster analysis indicated that Geumhogang 6 was the most contaminated site, while tributaries and most of the down stream sites of Nakdong River were mainly affected by each nutrients (Factor 1) and organic pollutants (Factor 2). The verification consequence of Confirmatory factor analysis (CFA) from Exploratory factor analysis (EFA) result can be summarized as follows: we could find additional relations between variables besides the structure from EFA, which we obtained through the second-order final modeling adopted in CFA. Nutrients had the biggest impact on water pollution for each sampling station and season. In particular, It was analyzed that P-series pollutant should be controlled during spring and winter and N-series pollutant should be controlled during summer and fall.